Atmospheric River Case Studies in the U.S

Total Page:16

File Type:pdf, Size:1020Kb

Atmospheric River Case Studies in the U.S Susquehanna University Scholarly Commons Senior Scholars Day Apr 27th, 12:00 AM - 12:00 AM Atmospheric River Case Studies in the U.S. Mid-Atlantic Region Jonathan Lewis Susquehanna University Follow this and additional works at: https://scholarlycommons.susqu.edu/ssd Part of the Atmospheric Sciences Commons, Climate Commons, and the Meteorology Commons Lewis, Jonathan, "Atmospheric River Case Studies in the U.S. Mid-Atlantic Region" (2021). Senior Scholars Day. 33. https://scholarlycommons.susqu.edu/ssd/2021/posters/33 This Event is brought to you for free and open access by Scholarly Commons. It has been accepted for inclusion in Senior Scholars Day by an authorized administrator of Scholarly Commons. For more information, please contact [email protected]. Atmospheric River Case Studies in the U.S. Mid-Atlantic Region Jonathan Lewis & Dr. Katherine Straub Department of Earth and Environmental Sciences Susquehanna University, Selinsgrove, PA Abstract Category 5 AR Events “Typical” AR 5 Case Study AR Climatology This case study is an extreme case of a typical AR that occurs in the mid-Atlantic region. Atmospheric Rivers (ARs) are narrow bands of atmospheric moisture that bring a significant Start Date End Date Max IVT (kg m-1 s-1) Length (Days) Occurring during the cold season from February Average Frequency of Each AR Category by Month from 2010-2020, Method 3 amount of precipitation to the impacted region. While ARs on the West Coast of the United 1/25/2010 1/25/2010 1626.2 1 6-7, 2020, this AR drew its moisture from the 7 9/26/2010 10/1/2010 1913.2 6 southwest and occurred ahead of a deep upper- States are more frequently analyzed, evaluation of the East Coast mid-Atlantic region is also 6 important in understanding this phenomenon. East Coast ARs can be studied using the 8/24/2011 8/29/2011 2650.6 6 level trough. Immediately after the low-level jet techniques already established to study West Coast ARs. Using 6-hourly MERRA-2 data with 9/18/2012 9/19/2012 1530.9 2 transporting high moisture air maximized, a 5 integrated water vapor transport (IVT) and integrated water vapor (IWV) thresholds, we 10/28/2012 10/30/2012 1960.6 3 steep drop in moisture and IVT occurred as the cold front passed through the region. 4 developed an extended scale to account for the longer duration ARs that occur, an algorithm 1/29/2013 1/31/2013 1591.4 3 IVT Amplitude to study AR events from 2010-2020, and a case study approach to analyze Category 5 mid- 3 4/6/2017 4/6/2017 1807.5 1 2000 Atlantic (38°N-42°N, 71.5°W-76.5°W) AR events. We developed 3 distinct methods for Average Number ARs of 6/15/2017 6/26/2017 1602.4 12 1800 2 analyzing ARs over the region. All 3 methods classify AR duration in the same manner, but the 10/29/2017 10/30/2017 2079.2 2 1600 differences stem from how IVT thresholds are identified and the inclusion (or not) of IWV as a 1 7/21/2018 8/5/2018 1595.6 16 1400 ) 1 threshold for AR identification. Method 1 identified 533 AR events from 2010-2020 with an - 10/6/2018 10/12/2018 1755.5 7 s 1200 0 1 average of 48.45 ARs/year. Method 2 identified 512 AR events with an average of 46.55 - Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 11/13/2018 11/13/2018 1664.5 1 1000 AR1 AR2 AR3 AR4 AR5 ARs/year. Method 3 identified 669 AR events with an average of 60.82 ARs/year. Method 1 800 1/24/2019 1/24/2019 1774.7 1 IVT (kg m was found to be unsatisfactory in its ability to capture ARs across the region. Methods 2 and 1/11/2020 1/12/2020 1743.9 2 600 • More ARs in warmer season vs. colder season • Longer ARs in summer may contain multiple ARs 3 are used in different applications and are both successful in identifying ARs. We identified 400 2/6/2020 2/7/2020 1896.6 2 • Moisture present in the summer helps to fuel • Category 1 (blue) and Category 2 (green) ARs 18 Category 5 AR events from 2010-2020 and classified them into 3 types of ARs: “Typical” 200 4/13/2020 4/13/2020 1716.1 1 additional ARs most frequent Events, Tropical Cyclone Events, and Summer Events. Each extreme case type has helped to 0 develop an understanding of East Coast ARs in the mid-Atlantic area. 9/25/2020 9/30/2020 1839.9 6 Average Length (Days) of Each AR Event by Month from 2010-2020, Method 3 12/24/2020 12/25/2020 1604.2 2 25 *All Cat 5 ARs in table identified by Method 3 • 3 Types of AR Events (colored in table above): 20 1. “Typical” (white): AR events occur in fall through spring ahead of surface low Identifying ARs pressure systems 15 2. Tropical Cyclone (green): Different way of thinking about hurricanes, tropical storms, and transition to extratropical events 10 Atmospheric River: Average Length (Days) 3. Summer (yellow): Summer AR events contain multiple moisture pulses A long, narrow, and 5 • Common features: transient corridor of strong ▪ Very high low-level specific humidity in all events 0 horizontal water ▪ Highest regional IVT amplitude about ¾ of the way through the life of the AR Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec vapor transport that is • Corresponding time stamp shows highest impact on mid-Atlantic region AR1 AR2 AR3 AR4 AR5 typically associated with ▪ Events occur ahead of distinctive 300 hPa troughs • Cold Season: • Warm Season: a low-level jet stream ▪ Deep tropospheric moisture present in all events ▪ Shorter ARs ▪ Longer ARs → Pulses → Multiple ARs? ▪ AR Category more likely achieved by a single ▪ AR Category more likely achieved by ahead of the cold front of ▪ High IVT: high IVT value above respective threshold consecutive low IVT values an extratropical cyclone. • Warm sector of low pressure system contains moist air The water vapor in • Strong winds from the south and/or southwest Average Frequency of Each AR Category by Year from 1980-2020, Method 2 60 240 atmospheric rivers is ▪ Distinct contrast in moisture depth between AR and post-cold front air mass 230 supplied by tropical and/or 50 220 extratropical moisture 40 210 sources (AMS, 2019). 200 Tropical Cyclone AR 5 Case Study Summer AR 5 Case Study 30 190 ) and IWV (dm) Amplitudes Number ARs of 1 - This AR is another way of thinking about and Summer AR events are often longer. This case s 20 180 1 • For this study, IVT and IWV are calculated - classifying tropical cyclones. This example study lasted from June 15-26, 2017 and drew its 170 over the mid-Atlantic region (see map). 10 occurred from August 24-29, 2011 and was moisture from multiple sources. During this IVT (kg m • Minimum requirements to be classified originally classified as Hurricane Irene. Irene time, both Tropical Storm Cindy, which was 160 as an AR event: formed in the western Atlantic, tracked along occurring in the Gulf of Mexico, and the 0 150 the East Coast, then became an extratropical Bermuda High were supplying moisture to the ▪ IVT > 250 kg m-1 s-1 and IWV > 2.0 cm cyclone as it passed over Vermont. The origin of region to fuel the AR. The multiple pulses in the AR 1 AR 2 AR 3 AR 4 AR 5 IVT Average IWV Average for > 24-hour duration, or this AR comes from the south, drawing its IVT Amplitude graph suggest multiple ARs • Increase in: • Category 1 and Category 2 ARs most frequent moisture from the Atlantic Ocean. occurring within the same system. ▪ IVT > 500 kg m-1 s-1 and IWV > 2.0 cm, ▪ Frequency of ARs • Frequency of Category 3, 4, and 5 ARs increasing IVT Amplitude IVT Amplitude ▪ Integrated water vapor transport (IVT) over last decade any duration 3000.0 1800.0 ▪ Integrated water vapor (IWV) • Connection to climate change? 1600.0 2500.0 1400.0 2000.0 1200.0 ) ) 1 1 - - s s 1 1 - - 1000.0 1500.0 800.0 References IVT IVT (kg m IVT IVT (kg m Method Comparisons 1000.0 600.0 400.0 American Meteorological Society (AMS). (2019, March 15). Atmospheric river. Glossary of Meteorology. 500.0 Retrieved December 23, 2020, from https://glossary.ametsoc.org/wiki/Atmospheric_river 200.0 Method 1 Method 2 Method 3 Cordeira, J. M., Kaminski, A. N., Metz, N. D., Duncan, M., Bachli, K., Ericksen, M., Glade, I., Roberts, C., & 0.0 Average IVT Average IVT and Maximum IVT and 0.0 Evans, C. (2020, January 14). A climatology of atmospheric rivers over the Northeast United Threshold Only Average IWV Thresholds Average IWV Thresholds States [Conference session]. 33rd Conference on Climate Variability and Change, Boston, MA, United States. https://ams.confex.com/ams/2020Annual/videogateway.cgi/id/516212?recordingid=516212 Frequency of Each AR Category by Year from 2010-2020, All 3 Methods Cordeira, J. M., Ralph, F. M., Martin, A., Gaggini, N., Spackman, J. R., Neiman, P. J., Rutz, J. J., & Pierce, R. 70 68 67 67 (2017). Forecasting atmospheric rivers during CalWater 2015. Bulletin of the American 64 62 Meteorological Society, 98(3), 449-459. https://doi.org/10.1175/BAMS-D-15-00245.1 60 60 59 60 58 Newell, R. E., Newell, N. E., Zhu, Y., & Scott, C. (1992).
Recommended publications
  • Southern Hemisphere Mid- and High-Latitudinal AOD, CO, NO2, And
    Ahn et al. Progress in Earth and Planetary Science (2019) 6:34 Progress in Earth and https://doi.org/10.1186/s40645-019-0277-y Planetary Science RESEARCH ARTICLE Open Access Southern Hemisphere mid- and high- latitudinal AOD, CO, NO2, and HCHO: spatiotemporal patterns revealed by satellite observations Dha Hyun Ahn1, Taejin Choi2, Jhoon Kim1, Sang Seo Park3, Yun Gon Lee4, Seong-Joong Kim2 and Ja-Ho Koo1* Abstract To assess air pollution emitted in Southern Hemisphere mid-latitudes and transported to Antarctica, we investigate the climatological mean and temporal trends in aerosol optical depth (AOD), carbon monoxide (CO), nitrogen dioxide (NO2), and formaldehyde (HCHO) columns using satellite observations. Generally, all these measurements exhibit sharp peaks over and near the three nearby inhabited continents: South America, Africa, and Australia. This pattern indicates the large emission effect of anthropogenic activities and biomass burning processes. High AOD is also found over the Southern Atlantic Ocean, probably because of the sea salt production driven by strong winds. Since the pristine Antarctic atmosphere can be polluted by transport of air pollutants from the mid-latitudes, we analyze the 10-day back trajectories that arrive at Antarctic ground stations in consideration of the spatial distribution of mid-latitudinal AOD, CO, NO2, and HCHO. We find that the influence of mid-latitudinal emission differs across Antarctic regions: western Antarctic regions show relatively more back trajectories from the mid-latitudes, while the eastern Antarctic regions do not show large intrusions of mid-latitudinal air masses. Finally, we estimate the long-term trends in AOD, CO, NO2, and HCHO during the past decade (2005–2016).
    [Show full text]
  • NWS Unified Surface Analysis Manual
    Unified Surface Analysis Manual Weather Prediction Center Ocean Prediction Center National Hurricane Center Honolulu Forecast Office November 21, 2013 Table of Contents Chapter 1: Surface Analysis – Its History at the Analysis Centers…………….3 Chapter 2: Datasets available for creation of the Unified Analysis………...…..5 Chapter 3: The Unified Surface Analysis and related features.……….……….19 Chapter 4: Creation/Merging of the Unified Surface Analysis………….……..24 Chapter 5: Bibliography………………………………………………….…….30 Appendix A: Unified Graphics Legend showing Ocean Center symbols.….…33 2 Chapter 1: Surface Analysis – Its History at the Analysis Centers 1. INTRODUCTION Since 1942, surface analyses produced by several different offices within the U.S. Weather Bureau (USWB) and the National Oceanic and Atmospheric Administration’s (NOAA’s) National Weather Service (NWS) were generally based on the Norwegian Cyclone Model (Bjerknes 1919) over land, and in recent decades, the Shapiro-Keyser Model over the mid-latitudes of the ocean. The graphic below shows a typical evolution according to both models of cyclone development. Conceptual models of cyclone evolution showing lower-tropospheric (e.g., 850-hPa) geopotential height and fronts (top), and lower-tropospheric potential temperature (bottom). (a) Norwegian cyclone model: (I) incipient frontal cyclone, (II) and (III) narrowing warm sector, (IV) occlusion; (b) Shapiro–Keyser cyclone model: (I) incipient frontal cyclone, (II) frontal fracture, (III) frontal T-bone and bent-back front, (IV) frontal T-bone and warm seclusion. Panel (b) is adapted from Shapiro and Keyser (1990) , their FIG. 10.27 ) to enhance the zonal elongation of the cyclone and fronts and to reflect the continued existence of the frontal T-bone in stage IV.
    [Show full text]
  • Extreme Weather Events
    Extreme weather events Introduction The further a particular weather event lies from the typical range of that type of event, the more it is likely to be described as an extreme event, irrespective of whether it concerns a violent storm, unusual temperatures, heavy precipitation, drought or flood. 2012 seems to have been a year of extreme weather events (‘superstorm’ Sandy in the USA, high rainfall and floods in the UK, etc.). Other years in the last decade have also contained droughts and wildfires (in the USA and Australia), hurricane Katrina (USA), floods (Pakistan) and heat waves (Russia and France). At the same time it is becoming increasingly accepted that human activity, principally the burning of fossil fuels, is changing the global climate and causing the atmosphere to warm. The average global temperature of the lowermost atmosphere has increased markedly since about 1980. Are the two observations, which operate on different timescales1, connected? Are extreme weather events really becoming more common and/or more severe, or are they perhaps part of the climate’s natural variability? The aim of this document is to investigate these two questions. Some basic physics of a warmer atmosphere As air warms its humidity is able to rise and so the atmosphere carries more water vapour. For example, the water content of the atmosphere increases by 7% for each degree Centigrade rise in temperature, although globally precipitation is expected to rise by only about 2%/°C because relative humidity is typically not expected to change on the global scale.8 1 climate change is defined as changes occurring at least over a few decades whereas extreme weather typically lasts from days to months.
    [Show full text]
  • Clima Te Change 2007 – Synthesis Repor T
    he Intergovernmental Panel on Climate Change (IPCC) was set up jointly by the World Meteorological Organization and the TUnited Nations Environment Programme to provide an authoritative international statement of scientific understanding of climate change. The IPCC’s periodic assessments of the causes, impacts and possible response strategies to climate change are the most comprehensive and up-to-date reports available on the subject, and form the standard reference for all concerned with climate change in academia, government and industry worldwide. This Synthesis Report is the fourth element of the IPCC Fourth Assessment Report “Climate Change 2007”. Through three working groups, many hundreds of international experts assess climate change in this Report. The three working group contributions are available from Cambridge University Press: Climate Change 2007 – The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the IPCC (ISBN 978 0521 88009-1 Hardback; 978 0521 70596-7 Paperback) Climate Change 2007 – Impacts, Adaptation and Vulnerability Contribution of Working Group II to the Fourth Assessment Report of the IPCC (978 0521 88010-7 Hardback; 978 0521 70597-4 Paperback) Climate Change 2007 – Mitigation of Climate Change CHANGE 2007 – SYNTHESIS REPORT CLIMATE Contribution of Working Group III to the Fourth Assessment Report of the IPCC (978 0521 88011-4 Hardback; 978 0521 70598-1 Paperback) Climate Change 2007 – Synthesis Report is based on the assessment carried out by the three Working Groups
    [Show full text]
  • Atmospheric Rivers: Harbors for Extreme Winter Precipitation by Zack Guido
    3 | Feature Article Atmospheric Rivers: Harbors for Extreme Winter Precipitation By Zack Guido ierce winds loaded with moisture Fblasted into the Southwest on Decem- ber 18, 2010, dumping record-setting rain and snow from Southern California to southern Colorado. Fourteen inches of rain drenched St. George, Utah, over six days, while 6 inches soaked parts of northwest Arizona in a torrent that sin- gle-handedly postponed drought. Behind this wet weather was a phe- nomenon called atmospheric rivers, a Figure 1. A satellite image of an atmospheric river striking the Pacific Northwest on term first coined in 1998. ARs, as they November 7, 2006. This event produced about 25 inches of rain in three days. Warm are known to scientists, often deliver colors in the image represent moist air and cool colors denote dry air. The horizontal extreme precipitation, mostly to the band of red and purple at the bottom of the image is the Intertropical Convergence West Coast, but sometimes inland as Zone (ITCZ), a normally moist area that some of the strongest ARs can tap into, as well, prompting researchers to probe how happened in this case. Photo credit: Marty Ralph. they form and the effects they have in a changing climate. They are products of an unevenly heated through March is the peak season for Earth and form during winter, when the ARs that drench Southern California. ARs have caused nearly all of the largest temperature difference between the trop- floods on record in California, account- ics and the poles is greatest. The most intense ARs can transport an ing for most of the $400 million the state amount of water vapor equal to the flow spends each year to repair flood damage.
    [Show full text]
  • AI4ESP1027 ( Many Types Including Tropical Cyclones Exhibit Greater Realism in High-Resolution, Multidecadal Simulations
    Tracking Extremes in Exascale Simulations Utilizing Exascale Platforms 1 Authors/Affiliations William D. Collins (LBNL and UC Berkeley) and the Calibrated and Systematic Characteriza- tion, Attribution, and Detection of Extremes (CASCADE) Scientific Focus Area (SFA) 2 Focal Area Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods 3 Science Challenge There is a growing recognition in the literature that understanding variability and trends in hy- drometeorological extremes relies on understanding variability and trends in the meteorological phenomena that drive these extremes. Such phenomenon-focused understanding relies critically on a robust methodology for identifying the occurrence of these phenomena in observations and model output, but a robust methodology does not currently exist. There are a variety of heuristic methods reported in the literature for identifying, and in some cases temporally tracking, meteo- rological phenomena. However, there have been several intercomparison projects (and resulting papers) indicating that there is a large uncertainty associated with choices in the identification methods; this is the case for extratropical cyclones (ETCs) [1], atmospheric rivers (ARs) [2], and even tropical cyclones (TCs) [3]; and we hypothesize that this is a general issue with heuristic identification methods altogether. These studies clearly show that this identification uncertainty leads to a large, and previously under-recognized, quantitative and even qualitative uncertainty in our understanding of these phenomena. In light of these issues, we suggest that the field could be advanced by addressing two overar- ching questions. First, can we explicitly quantify uncertainty associated with detecting hydrom- eteorological phenomena? Second, can we decompose detection uncertainty into reducible and irreducible parts? 4 Rationale Anthropogenically-forced climate changes in the number and character of extreme storms have the potential to significantly impact human and natural systems.
    [Show full text]
  • Atmospheric Rivers
    Atmospheric Rivers What is an Atmospheric River? Atmospheric rivers are relatively narrow regions in the atmosphere that are responsible for most of the transport of water vapor from the tropics. Atmospheric rivers come in all shapes and sizes but those that contain the largest amounts of water vapor and strongest winds are responsible for extreme rainfall events and floods. This type of hydrologic event can affect the entire west coast of North America. These extreme events can disrupt travel, induce mudslides, and cause damage to life and property. Not all atmospheric rivers are disruptive. Many are weak and provide beneficial rain or high elevation snow that is crucial to the water supply. The image on the left shows an atmospheric river that affected South- east Alaska on 11-08-2014. The atmospheric river is marked by the narrow plume of subtropical moisture evident in the Total Precipitable Water field extending from the central Pacific northeastward through the Gulf of Alaska. Why do Atmospheric Rivers Occur in SE Alaska? Due to its location on the western side of the North American continent, SE Alaska is often the target for powerful ocean storms that form over the western and central Pacific Ocean and move eastward, steered by the prevailing westerly upper level jet stream. These powerful low pressure systems often have strong fronts associated with them. Fronts act like a conduit to channel warm, moist air northward and eastward ahead of the low pressure system in what is called the “warm conveyor belt”. The strongest fronts are also regions of strong winds in the lower portions of the atmosphere.
    [Show full text]
  • A Case Study of Four Atmospheric River Events Over the Pacific West Coast of the United States Isaac Arseneau1, Dr
    A Case Study of Four Atmospheric River Events Over the Pacific West Coast of the United States Isaac Arseneau1, Dr. Wendell Nuss2 1Valparaiso University OCE 1659628 Abstract 2Naval Postgraduate School Atmospheric Rivers (AR) are moisture phenomena related to cyclones which bring moisture and large amounts of precipitation to areas of enhanced elevation along coastal areas. These events bring much of the rain received by the state of California, and the past winter many AR events brought much-needed rain to the region. Four different events from the 2016 fall through 2017 spring seasons are examined to better identify the relative roles of long-range moisture transport versus local moisture fluxes in AR events. Cross-sections of areas and times of interest during each event are generated, along with trajectory analyses of each event which will aid in determining the origin of the moisture being moved over land. Both the cross-sections and the trajectory analysis are taken from the CFSR (Climate Forecast System Reanalysis) model. It is expected that the results of these processes will support the findings of Dacre et al. (2015), which show that the moisture anomaly present during AR events is not actually due to moisture transport directly along the AR itself. Rather, the AR is the result of moisture convergence due to a combination of the warm conveyor belt forcing the ascent of moisture over the warm front and the trailing cold front forcing ascent as it closes the gap between itself and the warm front. The importance of this research is first and foremost evident in the California region, as water conservation in naturally dry areas is extremely important to the ever-expanding cities and communities present there and October 13, 2016 January 17, 2016 February 7, 2016 April 5, 2016 require long-term planning.
    [Show full text]
  • Inland Impacts of Atmospheric River and Tropical Cyclone Extremes on Nitrate Transport and Stable Isotope Measurements
    Environmental Earth Sciences (2019) 78:36 https://doi.org/10.1007/s12665-018-8018-x THEMATIC ISSUE Inland impacts of atmospheric river and tropical cyclone extremes on nitrate transport and stable isotope measurements A. Husic1 · J. Fox2 · E. Adams2 · J. Backus3 · E. Pollock4 · W. Ford5 · C. Agouridis5 Received: 30 June 2018 / Accepted: 19 December 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Atmospheric rivers and tropical cyclones originate in the tropics and can transport high rainfall amounts to inland temper- − ate regions. The purpose of this study was to investigate the response of nitrate (NO3 ) pathways, concentration peaks, and 15 18 2 18 13 stable isotope (δ NNO3, δ ONO3, δ HH2O, δ OH2O, and δ CDIC) measurements to these extreme events. A tropical cyclone and atmospheric river produced the number one and four ranked events in 2017, respectively, at a Kentucky USA watershed characterized by mature karst topography. Hydrologic responses from the two events were different due to rainfall character- istics with the tropical cyclone producing a steeper rising limb of the spring hydrograph and greater runoff generation to the 2 18 surface stream compared to the atmospheric river. Local minima and maxima of specific conductance, δ HH2O, δ OH2O, and 13 − 15 18 δ CDIC coincided with hydrograph peaks for both events. Minima and maxima of NO 3 , δ NNO3, δ ONO3, and temperature lagged behind the hydrograph peak for both events, and the values continued to be impacted by diffuse recharge during − hydrograph recession. Quick-flow pathways accounted for less than 20% of the total NO3 yield, while intermediate (30%) and slow-flow (50%) pathways composed the remaining load.
    [Show full text]
  • Alternative Earth Science Datasets for Identifying Patterns and Events
    https://ntrs.nasa.gov/search.jsp?R=20190002267 2020-02-17T17:17:45+00:00Z Alternative Earth Science Datasets For Identifying Patterns and Events Kaylin Bugbee1, Robert Griffin1, Brian Freitag1, Jeffrey Miller1, Rahul Ramachandran2, and Jia Zhang3 (1) University of Alabama in Huntsville (2) NASA MSFC (3) Carnegie Mellon Universityv Earth Observation Big Data • Earth observation data volumes are growing exponentially • NOAA collects about 7 terabytes of data per day1 • Adds to existing 25 PB archive • Upcoming missions will generate another 5 TB per day • NASA’s Earth observation data is expected to grow to 131 TB of data per day by 20222 • NISAR and other large data volume missions3 Over the next five years, the daily ingest of data into the • Other agencies like ESA expect data EOSDIS archive is expected to grow significantly, to more 4 than 131 terabytes (TB) of forward processing. NASA volumes to continue to grow EOSDIS image. • How do we effectively explore and search through these large amounts of data? Alternative Data • Data which are extracted or generated from non-traditional sources • Social media data • Point of sale transactions • Product reviews • Logistics • Idea originates in investment world • Include alternative data sources in investment decision making process • Earth observation data is a growing Image Credit: NASA alternative data source for investing • DMSP and VIIRS nightlight data Alternative Data for Earth Science • Are there alternative data sources in the Earth sciences that can be used in a similar manner? •
    [Show full text]
  • A Revised Tornado Definition and Changes in Tornado Taxonomy
    1256 WEATHER AND FORECASTING VOLUME 29 A Revised Tornado Definition and Changes in Tornado Taxonomy ERNEST M. AGEE Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, Indiana (Manuscript received 4 June 2014, in final form 30 July 2014) ABSTRACT The tornado taxonomy presented by Agee and Jones is revised to account for the new definition of a tor- nado provided by the American Meteorological Society (AMS) in October 2013, resulting in the elimination of shear-driven vortices from the taxonomy, such as gustnadoes and vortices in the eyewall of hurricanes. Other relevant research findings since the initial issuance of the taxonomy are also considered and in- corporated, where appropriate, to help improve the classification system. Multiple misoscale shear-driven vortices in a single tornado event, when resulting from an inertial instability, are also viewed to not meet the definition of a tornado. 1. Introduction and considerations from a cumuliform cloud, and often visible as a funnel cloud and/or circulating debris/dust at the ground.’’ In The first proposed tornado taxonomy was presented view of the latest definition, a few changes are warranted by Agee and Jones (2009, hereafter AJ) consisting of in the AJ taxonomy. Considering the roles played by three types and 15 species, ranging from the type I buoyancy and shear on a variety of spatial and temporal (potentially strong and violent) tornadoes produced by scales (from miso to meso to synoptic), coupled with the the classic supercell, to the more benign type III con- requirement in the latest definition that a tornado must vective and shear-driven vortices such as landspouts and be pendant from a cumuliform cloud, it is necessary to gustnadoes.
    [Show full text]
  • Chapter 1 NWP (EES 753) (Reference) (Based on Lin 2007; Kalnay 2003; Yu Lec
    Chapter 1 NWP (EES 753) (reference) (Based on Lin 2007; Kalnay 2003; Yu Lec. Note) Chapter 1 Introduction and Historical Review 1.0 Introduction Basically, numerical weather prediction uses numerical methods to approximate a set of partially differential equations on discrete grid points in a finite area to predict the weather systems and processes in a finite area for a certain time in the future. In order to numerically integrate the partial differential equations, which govern the atmospheric motions and processes, with time, one needs to start the integration at certain time. In order to do so, the meteorological variables need to be prescribed at this initial time, which are called initial conditions. Mathematically, this corresponds to solve an initial-value problem. Due to practical limitations, such as computing power, numerical methods, etc., we are forced to make the numerical integration for predicting weather systems in a finite area. In order to do so, it is necessary to specify the meteorological variables at the boundaries, which include upper, lower, and lateral boundaries, of the domain of interest. Mathematically, this corresponds to solve a boundary- value problem. Thus, mathematically, numerical weather prediction is equivalent to solving an initial- and boundary- value problem. For example, to solve the following simple one-dimensional partial differential equation, u u U F(t, x) , (1.1) t x where u is the horizontal wind speed in x-direction, U the constant basic or mean wind speed, and F(t, x) is a forcing function, it is necessary to specify the , the variable to be predicted, at an initial time, say to .
    [Show full text]